This invention is in the field of oil and gas production. Embodiments of this invention are more specifically directed to the analysis of secondary recovery actions in maximizing oil and gas output.
The current economic climate emphasizes the need for optimizing hydrocarbon production. Such optimization is especially important considering that the costs of drilling new wells and operating existing wells are high by historical standards, largely because of the extreme depths to which new producing wells must be drilled and because of other physical barriers to discovering and exploiting reservoirs; those reservoirs that are easy to reach have already been developed and produced. These high economic stakes require operators to devote substantial resources toward effective management of oil and gas reservoirs, and effective management of individual wells within production fields.
As known in the art, an important secondary recovery operation injects water, gas, or other fluids into the reservoir at one or more injection wells, commonly referred to as “waterflood”. In theory, this injection increases the pressure in producing wells that are connected to the injection wells via the reservoir, thus producing oil and gas at increased flow rates. In planning and managing secondary recovery operations, the operator is faced with decisions regarding whether to initiate or cease such operations, and also how many wells are to serve as injection wells and their locations in the field, to maximize production at minimum cost.
As known in the art, the optimization of a production field is a complex problem, involving many variables and presenting many choices, exacerbated by the complexity and inscrutability of the sub-surface “architecture” of today's producing reservoirs. Especially for those reservoirs at extreme depths, or located in difficult or inaccessible land or offshore locations, the precision and accuracy of the necessarily indirect methods used to characterize the structure and location of the hydrocarbon-bearing reservoirs is necessarily limited. In addition, the sub-surface structure of many reservoirs presents complexities such as variable porosity and permeability of the rock; fractures and faults that compartmentalize formations may also be present in the reservoir, further complicating sub-surface fluid flow. Models and numerical techniques for estimating and analyzing the effect of injection at one well, on the flow rates at one or more producing wells, are desirable tools toward solving this complex problem of production optimization.
One class of models for analyzing the effects of waterflood injection are known in the art as “capacitance models”, or “capacitance-resistivity models”. Examples of these models are described in Liang et al., “Optimization of Oil Production Based on a Capacitance Model of Production and Injection Rates”, SPE 107713, presented at the 2007 SPE Hydrocarbon Economics and Evaluation Symposium (2007); Sayarpour et al., “The Use of Capacitance-resistivity Models for Rapid Estimation of Waterflood Performance and Optimization”, SPE 110081, presented at the 2007 SPE Annual Technical Conference and Exhibition (2007); and Kaviani et al., “Estimation of Interwell Connectivity in the Case of Fluctuating Bottomhole Pressures”, SPE 117856, presented at the 2008 Abu Dhabi International Exhibition and Conference (2008). In a general sense, the capacitance-resistivity model (“CRM”) is the result of a regression (e.g., multivariate linear regression) applied to injector well flow rates and producing well flow rates, to express the cumulative production rate at a producing well over time as the sum of a primary production term (typically an exponential from an initial production rate value), a term expressing the effect of changes in the bottomhole pressure (BHP) at the producing well itself, and a third term corresponding to the flow rate at an injector multiplied by an interwell connectivity coefficient for the path between the injector and the producing well of interest, summed over all relevant injectors in the field. Such a model enables evaluation of changes in the output at a producing well, in response to changes in injection rate at one or more injectors.
Of course, modern production fields generally involve more than one producing well, each responding to injection at one or more injector wells. In other words, the flow from a given injector will be non-uniformly distributed by the formation to the various producing wells; in addition, producer-producer effects can also be present, in which increased production at one producing well affects the production at another producing well (e.g., by locally reducing reservoir pressure at the affected well). These mechanisms prohibit CRM evaluation at each well individually—rather, the definition and evaluation of the model requires the regression to be simultaneously performed over all producing wells relative to all injecting wells. Considering that conventional capacitance-resistivity models use three parameters for each injector-producer well combination, even a modestly-sized field will necessitate convergence of the model over a relatively large number of parameters. As a result, the CRM is necessarily over-parameterized, often resulting in the inability to reach a reasonable solution when applied to realistic production fields. Even with modern computational resources, this operation is, at best, quite time-consuming and inefficient.
For mature production fields, well flow rates over time provide a significant source of data useful in deriving a connectivity model. In some cases, flow rates over time for both producing and injecting wells are directly available; in other cases, downhole or wellhead pressure and temperature measurements are available, from which flow rates may be inferred. Again, for even a modestly-sized production field, the amount of these data can rapidly become overwhelming. Rigorous numerical analysis of these data in defining and evaluating a connectivity or response model (e.g., CRM) consumes substantial computing time and resources. These large data sets and the complex interaction of the flows among the injectors and producers render it difficult for a human user or for an automated numerical system to identify causal relationships between injection events and produced fluids.
By way of further background, U.S. Pat. No. 7,890,200, issued Feb. 15, 2011, entitled “Process-Related Systems and Methods”, commonly assigned herewith and incorporated herein by reference in its entirety, describes a system and method for monitoring values of multiple process variables over time, and identifying causal relationships among the process variables, including identification of cause events in one process variable and corresponding response events in another process variable. According to this patent, the system and method also associate confidence levels for the identified events.